Incremental Learning of Full-Pose Via-Point Movement Primitives on Riemannian Manifolds

• DOI: 10.48550/arxiv.2312.08030
• Publication Date: 2024-05-13
• Authors: Daab, Tilman; Jaquier, Noémie; Dreher, Christian; Meixner, Andre; Krebs, Franziska; Asfour, Tamim
• Keywords: FOS: Computer and information sciences; Computer Science - Robotics; Robotics (cs.RO)

Movement primitives (MPs) are compact representations of robot skills that can be learned from demonstrations and combined into complex behaviors. However, merely equipping robots with a fixed set of innate MPs is insufficient to deploy them in dynamic and unpredictable environments. Instead, the full potential of MPs remains to be attained via adaptable, large-scale MP libraries. In this paper, we propose a set of seven fundamental operations to incrementally learn, improve, and re-organize MP libraries. To showcase their applicability, we provide explicit formulations of the spatial operations for libraries composed of Via-Point Movement Primitives (VMPs). By building on Riemannian manifold theory, our approach enables the incremental learning of all parameters of position and orientation VMPs within a library. Moreover, our approach stores a fixed number of parameters, thus complying with the essential principles of incremental learning. We evaluate our approach to incrementally learn a VMP library from motion capture data provided sequentially.<br/>This work has been submitted to the IEEE for possible publication. 7 pages, 7 figures and 2 tables<br/>